1. Field of the Invention
The present invention relates to techniques for gathering information from sensors. More specifically, the present invention relates to a method and apparatus that uses statistical techniques to determine if a sensor in an ad hoc network is observing an interesting event which is worthwhile to transmit across the ad hoc network.
2. Related Art
“Smart motes” belong to class of integrated intelligent wireless sensors. These integrated intelligent wireless sensors typically include a number of components such as transducers, an operating system, and a central processing unit (CPU), which can preprocess sampled signals. Smart motes are quite useful because they can be organized into geographically distributed ad hoc networks, which can measure a wide range of physical variables such as: temperature, vibration, humidity, barometric pressure, radiation level, light, and sonar.
Many of the physical variables which are measured by smart motes are episodic in nature. Typically, there is some amount of normal background variation, which does not contain useful information. This background variation is interspersed with episodes of interesting events, which are characterized by elevated mean signal levels, increased burstiness, appearance of a trend in variables that are otherwise statistically stationary, or appearance of dynamic phenomena that distinguish the interesting events from the normal background variation. For example, FIG. 2 illustrates a signal with background variations and a real event. Background signals 202 and 206 are caused by background variations, whereas signal 204 is caused by a real event.
Although the smart motes can transmit data values continuously, doing so during uninteresting time periods wastes network bandwidth and battery power. One technique for using the bandwidth more effectively is to set thresholds for specific smart mote variables. If the measured level of a variable exceeds a threshold, the smart mote transmits the data. This “threshold-limit” technique suffers from two limitations:                1. It is difficult to decide where to set the threshold. For noisy processes, setting the threshold too low results in frequent “false-alarm” warnings. However, setting the threshold too high results in missing a real event.        2. The received data has gaps during the “uninteresting” times, but most pattern recognition techniques require uniformly sampled signals.        
Hence, what is needed is a method and an apparatus for selectively transmitting data from a remote sensor, such as a smart mote.